Identifying unseen content of interest

ABSTRACT

Making an information retrieval process public, so that it can be followed by others, allows capturing of an interest graph that allows people to learn more about shared interests with other people. This also allows items of interest to a trusted resource (such as an expert) to be identified. These items can then be brought to the attention of other users that share the same interest as the expert. In addition, by keeping track of what particular content a user has already seen, the system can bring items of interest to the user&#39;s attention, where the user has not yet seen those items.

BACKGROUND

Social network sites are currently popular. Many social network sitesbasically attempt to capture a social graph of connections among users.The users are often family members, classmates, and other prioracquaintances.

Current information retrieval systems allow individual users to employsearch engines to explore various areas of knowledge stored in a datacorpus, or a variety of different corpora, and accessible eitherdirectly, or over a network. For instance, some information retrievalsearch engines allow a user to submit a query to search for informationover a wide area network, such as the Internet. Conventionally, a usermay submit queries that represent topics of interest to that user.

Searches using these conventional types of search engines are private,in that the originator of the query, and in fact the queries themselvesalong with their search results, are not automatically shared withanyone else. If the user does wish to share this type of information, itis currently done by a manual, user-initiated, process which can befairly cumbersome and error prone.

In addition, while current search engines allow users to view relevantcontent, they do not promote relevant content that the user has not yetseen and that has been identified as interesting by a trusted resource.Therefore, users of conventional search engines must often sift throughsearch results that they have already seen, or search results that arenot necessarily of interest or that have not been labeled by any trustedresource as being particularly relevant.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

Current social network sites encounter problems in allowing users tolearn more about (and construct deeper relationships based on) sharedinterests with other people. Because information retrieval searchsystems are often used by users to search for information which is ofinterest to them, the searches, and the results that those usersselected in response to the searches, often yield a great deal ofknowledge about the current interests of the individuals using theinformation retrieval system. By making the information retrievalprocess public, so that it can be followed by others, an interest graphcan be captured that allows people to learn more about shared interestswith other people. It also allows items of interest to a trustedresource (such as an expert) to be identified. These items can then bebrought to the attention of other users that share the same interest asthe expert. In addition, by keeping track of what particular content auser has already seen, the system can bring items of interest to theuser's attention, where the user has not yet seen those items.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to implementationsthat solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram of a public search system, inaccordance with one embodiment.

FIG. 2 is a simplified flow diagram illustrating one embodiment of theoperation of the system shown in FIG. 1 .

FIGS. 3A-3C are exemplary embodiments of user interface displays.

FIG. 4A is a flow diagram showing one embodiment of processing clickdata.

FIGS. 4B and 5 show exemplary embodiments of user interface displays.

FIG. 6 is a more detailed block diagram of a public search system, inaccordance with one embodiment.

FIG. 7 is a more detailed flow diagram illustrating one embodiment ofthe operation of the system shown in FIG. 6 .

FIG. 8 is a flow diagram illustrating one embodiment for processing aquery.

FIG. 8A illustrates one embodiment of information stored in a topic andstatistics data store.

FIG. 8B illustrates one embodiment of information items contained in anexemplary record for a post

FIG. 9 is a more detailed flow diagram showing one embodiment forprocessing click data.

FIG. 10 is a simplified block diagram of an interest tracking component,in accordance with one embodiment.

FIG. 11 is a simplified flow diagram illustrating one embodiment of theoperation of the interest tracking component shown in FIG. 10 .

FIG. 12 illustrates one embodiment for processing a message input.

FIG. 13 shows one embodiment of a portion of the search system used forsurfacing unseen content of interest.

FIG. 14 is a flow diagram illustrating one embodiment of the operationof the system shown in FIG. 13 .

FIG. 15 is a flow diagram illustrating embodiments of how to identifycontent of interest.

FIG. 16 is a flow diagram illustrating embodiments of how to show unseencontent of interest to a user.

FIG. 17 is a flow diagram showing one embodiment of assisting a user insearching.

FIG. 18 is a block diagram of one illustrative computing environment inwhich the public search system can be implemented.

DETAILED DESCRIPTION General Operation

FIG. 1 is a simplified block diagram of one embodiment of a socialnetwork 8 that includes public search system 10. Public search system 10illustratively includes topic feed generator 12, feed distributorcomponent 14, search component 16, processor 18 and unseen content ofinterest tracking component 19. Public search system 10 is also shownconnected to a topic and statistics data store 20. In the embodimentshown in FIG. 1 , public search system 10 is also illustrativelyconnected to user interface component 22 which resides on a clientdevice. The client device can be any suitable computing device, such asa laptop computer, a cellular telephone, any other type of personaldigital assistant (PDA), other mobile device, or other computing device(such as a desktop computer).

In the embodiment shown in FIG. 1 , public search system 10 is shownconnected to user interface component 22 through network 24. Network 24can be a local area network, a wide-area network (such as the Internet)or any other desired network. Of course, user interface component 22could also be directly connected to, or reside on, public search system10. FIG. 1 also shows that public search system 10 is connected tosearch engine 26 which, itself, is connected either through a network28, or directly, to a corpus 30 that is to be searched.

Unseen content of interest tracking component 19 is discussed in greaterdetail below with respect to FIGS. 13-17 . Briefly, however, in oneembodiment, it facilitates identifying experts in different subjectmatter areas and also areas of interest for a given user. It thenidentifies content of interest to the given user and determines whetherthat content has been seen yet, by the user. If not, component 19presents the unseen content of interest to the user at an appropriatetime. Before describing component 19 in more detail, some other portionsof network 8 and system 10 will be described for purposes of betterunderstanding of one illustrative context in which component 19 can bedeployed.

It will be appreciated that the block diagram shown in FIG. 1 isexemplary only. The functions associated with the elements to bedescribed can be combined into a single component, or further dividedinto more discrete components. Similarly, the connections shown in FIG.1 can be through networks, or direct connections, and those shown arefor exemplary purposes only.

FIG. 2 is a simplified flow diagram illustrating one embodiment of theoperation of social network 8 shown in FIG. 1 . FIGS. 3A-3C showillustrative user interface displays corresponding to the operation ofthe system described with respect to FIG. 2 . FIGS. 1-3C will bedescribed in conjunction with one another.

User interface component 22 illustratively resides on a user's system,which may be a client device. In one embodiment, in order to use system8, a user first engages user interface component 22 to set up an accountwhich includes, for example, a user name and password. The user inputsthese items through interface component 22, and they are stored in topicand statistics data store 20. The user is illustratively able toidentify topics of interest which the user wishes to follow, orindividual users or groups of users that the user wishes to follow aswell. As discussed below with respect to FIGS. 10 and 13-16 , theseexplicit indications of topics of interest can be considered bycomponent 19 in identifying content of interest for the user. Thisinformation is also stored in data store 20. This can all be donethrough user interface displays generated by component 22.

Once this is done, and the user wishes to use system 8, the userillustratively logs on to system 8, through an authentication component(which is described in greater detail below), and user interfacecomponent 22 generates a user interface display 40 such as that shown inFIG. 3A. In the illustrative user interface display 40, the user's username is John Doe and that is displayed generally at 42, along with animage 44 which can be selected by John Doe to represent his user name.The display also presents a search box 46, which is a text box thatallows the user to enter text (such as by using a keyboard) thatrepresents a search query that the user wishes to have executed.Interface display 40 also illustratively displays the user names ortopics that user 42 is following. This is generally indicated at 48.User interface display 40 may also illustratively list other users thatare following user 42. This is generally indicated at 50. In addition,user interface display 40 displays a public stream of information 52,which has already been generated. The public stream 52 illustrativelyincludes a plurality of posts 54, corresponding to received topic feeds70 which will be described in greater detail below. Further, userinterface display 40 illustratively includes a set of actuable elementsgenerally shown at 200. By actuable (or actuatable) elements, it ismeant that the elements can be actuated through a suitable userinterface operation, such as by clicking on an element using a pointingdevice (like a mouse) or double-clicking or otherwise. These aredescribed in greater detail below as well.

When the interface display 40 is displayed by user interface component22, the user can enter a desired query into textbox 46. In the exampleshown in FIG. 3A, the user has typed in “stories about Paul Bunyan”.This corresponds to query 60 shown in FIG. 1 . The query is sent fromuser interface component 22 to public search component 10, andspecifically to topic feed generator 12. Receipt of query 60 by publicsearch system 10 is illustrated by block 62 in FIG. 2 .

Topic feed generator 12, in response to receiving query 60, generates atopic feed that includes query 60 and that is to be output in the publicstream 52 as a topic feed 70. Generating the topic feed 70, includingthe query 60, is indicated by block 72 in FIG. 2 .

Feed distributor component 14 then accesses data store 20 to identifythe followers of both John Doe (the user that submitted query 60) andthe followers of the subject matter content of the query 60, itself. Forinstance, the subject matter content of query 60 is illustratively “PaulBunyan”. Therefore, if any users have indicated that they wish to followthe topic category (or subject matter category) “Paul Bunyan”, then theywould be identified by feed distributor component 14 as a recipient oftopic feed 70 as well. Feed distributor component 14 then distributes orpublishes the topic feed 70 to those recipients that were identified.Identifying recipients is indicated by block 73 in FIG. 2 , anddistributing the topic feed 70 to the recipients is indicated by block74 in FIG. 2 . It can thus be seen that upon submission of query 60,system 8 automatically publishes that query in a topic feed to allrelevant recipients, without any further input from the user.

The distribution or publication can be done in other ways as well. Forinstance, feed distribution component 14 can wait to update the systemof a recipient until the recipient logs on to the system or otherwiseengages the system. Similarly, the feed distribution component 14 canwait to distribute topic feed 70 to recipients until after the user hasinteracted with the results from the query (as described below).

It should be noted that, in FIG. 3A, a wide variety of other embodimentscan be used. For instance, public stream 52 may be divided into twostreams, one which reflects posts from people that the user is followingand the other that reflects posts from topic areas that the user isfollowing. Of course, a wide variety of other changes can be made to thedisplay shown in FIG. 3A, as well.

Once the topic feed 70 has been distributed and published to theidentified recipients, a user interface component 22 (corresponding tothe recipients) illustratively generates a display for those recipients,such as shown in FIG. 3B. FIG. 3B is similar to that shown in FIG. 3A,except that the user 42 is indicated as Jane Deer. It can be seen fromFIG. 3A that Jane Deer is one of the followers of John Doe. Therefore,the topic feed 70 generated from any activity of John Doe will bedistributed to, and published at, a user interface component 22 residingat Jane Deer's device.

The topic feed 70 is posted as a post 54 on the public stream 52 of theuser interface display shown in FIG. 3B. It can be seen in FIG. 3B thatthe public stream 52 includes the post “John Doe searched for storiesabout Paul Bunyan”. FIG. 3B shows that both the source of the post andthe search which is the subject matter of the post are actuable links,and this is indicated by boxes 90 and 92 in FIG. 3B. Therefore, the term“John Doe” is included in box 90 and the query “stories about PaulBunyan” is included in box 92. If the user of the system that generatedthe display in FIG. 3B (that is, Jane Deer) clicks on the text in eitherbox 90 or 92, then the user's system takes action. If the user clicks onbox 90, which contains the source of the post, then the user's systemlinks the user to the home page of the person identified in box 90 (JohnDoe). Therefore, if Jane Deer clicks on box 90 that includes “John Doe”,then Jane Deer's system navigates to the home page for John Doe, andpresents Jane Deer with a user interface display such as that shown inFIG. 3A. If Jane Deer clicks on box 92, the results for that query willbe returned to Jane Deer. This will be described in more detail below.

At the same time that feed distributor component 14 is distributing thetopic feed generated by generator 12, search component 16 is alsoproviding query 60 to search engine 26 for execution against corpus 30.Search engine 26 may illustratively be a conventional informationretrieval search engine that searches the web for content associatedwith the query that was input. Search engine 26 can alternatively beimplemented in search component 16. Search engine 26 executes the searchagainst corpus 30 and returns search results 80 to search component 16in public search system 10. Search component 16 then returns results 80to user interface component 22 corresponding to the author of the query60 (that is, corresponding to John Doe).

Not only does search component 16 pass query 60 on to search engine 26for execution against corpus 30, but search component 16 also searchesthe records stored in data store 20 for any other posts that arerelevant to the subject matter of query 60. It may be that John Doe orother users of public search system 10 have submitted similar queries,and therefore topic feeds 70 may have already been generated for thosesimilar queries. Thus, search component 16 searches data store 20 forposts from previously generated topic feeds 70 that are relevant toquery 60. These are returned to the user through user interfacecomponent 22 as stream results 81. In other embodiments, the recordsreturned from searching data store 20 can be used to re-order searchresults 80 returned from search engine 26 or a search engine other thansearch engine 26.

User interface component 22 then generates a display 98 for the user(who submitted the query) such as that shown in FIG. 3C. The displayshown in FIG. 3C is similar to that shown in FIG. 3A, and similar itemsare similarly numbered. However, there are a number of differences. Itcan be seen that FIG. 3C shows that the search results are presented intwo separate categories. The first is stream results section 100 and thesecond is web results section 102. Under web results section 102, thesearch results 80 generated by search engine 26 are presented to theuser as user actuable links. By way of example, one of results 80 is aURL entitled “Paul and Babe in Bemidji, Minn.”. It is shown in a box 103to indicate that it is actuable on display 98. That is, if the userclicks on one of the results 80, the user will be taken to the web page,or other corpus entry, that spawned that search result.

Under stream results section 100, user interface display 98 lists allposts which contain search results 81 relevant to query 60. That is, ifdata store 20 included posts that were relevant to the query 60, thoseposts are also displayed in the stream results 81, along with the webresults 80. Again, to the extent that there are any actuable links instream results 81, posted in stream results section 100, the user cansimply click on those actuable links and be taken to the underlyingsource that spawned the link.

FIG. 3C also shows that system 8 can suggest additional searchstrategies. This is shown generally at 105.

Sharing Activity

FIGS. 4A-5 illustrate yet another embodiment. In the embodiment shown inFIGS. 4A-5 , not only is the public stream 52 filled with topic feeds 70that contain queries, but it also contains other search activities byusers, such as whether the user clicked on one of the results 80 or 81returned in response to a query 60, or whether the user actuated any ofthe links in the public stream 52. FIG. 4A is a flow diagramillustrating one embodiment of the operation of the system shown in FIG.1 , where a user (e.g., Jane Deer) that has received topic feed 70actuates one of the links in one of the posts in topic feed 70.

By way of example, assume that John Doe had clicked on one of the searchresults, such as result 103, that was presented in response to the query60. In that case, the user interface display 120 generated at JaneDeer's device is updated to look like that shown in FIG. 4B. That is, itwould not only show that John Doe had searched for stories about PaulBunyan, but it would also indicate that John Doe clicked on (or actuateda link for) one of the search results 103. In the embodiment shown inFIG. 4B, display 120 also shows that the public stream 52 has beenupdated to indicate that John Doe clicked on the particular URL “Pauland Babe in Bemidji, Minn.” that is highlighted by box 122 to indicatethat it is also actuable by Jane Deer.

One embodiment of the operation of system 8 in generating this type ofpost is shown in FIG. 4A. First, FIG. 4A shows that public search system10 receives either a click on a query or a result that was previouslydisplayed in public stream 52 by user interface component 22. That is,assume that John Doe clicked either on a query in his public stream 52or (in this case) one of the search results 103 displayed in FIG. 3C.This information is conveyed to public search system 10 as illustratedby block 150 in FIG. 4 .

Topic feed generator 12 then generates a topic feed that includes eitherthe query clicked on by John Doe, or, in this case, the result 103 fromweb results 80 that was clicked on by John Doe. Generating the topicfeed, including the actuated result, is indicated by block 152 in FIG.4A.

Feed distributor component 14 then identifies recipients of the topicfeed just generated, and distributes or publishes the topic feedgenerated in block 152 to those recipients. This is indicated by blocks154 and 156. Therefore, as shown in FIG. 4B, Jane Deer's user interfacedisplay 120 is updated with an additional post to the public stream 52which shows that not only has John Doe 90 searched for “stories aboutPaul Bunyan”, but he actually clicked on one of the results 80 returnedin response to that query, namely a URL entitled “Paul and Babe inBemidji, Minn.” 103, shown in block 122 in user interface display 120.

In response to John Doe clicking on that result, search component 16 andsearch engine 26 are used to return the document or page that spawnedthe link in box 122, to John Doe over user interface component 22, forviewing. This is indicated by block 158 in FIG. 4A.

While FIG. 4A has been described with respect to John Doe clicking onone of the search results 80 that was returned in response to the query60, the same action is taken if any other user clicked on an actuablelink in their public stream 52. For instance, if Jane Deer is presentedwith the user interface display 120 shown in FIG. 4B, Jane Deer can thenclick on the query “stories about Paul Bunyan” 92 or on the result “Pauland Babe in Bemidji, Minn.” shown in box 122, and public search system10 will generate a topic feed 70 for that activity as well. That is,assuming that Jane Deer has clicked on the query in box 92, topic feedgenerator 12 will generate a topic feed that includes that query, andfeed distributor component 14 will distribute the topic feed to allidentified recipients for that topic feed. Similarly, search component16 and search engine 26 will return the results 80 of the actuated queryto the user interface component 22 used by Jane Deer and that will bedisplayed to John Doe, in a similar fashion to that shown in FIG. 3C(where they were displayed for John Doe) in the first instance.

Similarly, if Jane Deer were to instead click on the result in box 122,then John Doe's user interface display would be updated to show that aswell. This is because John Doe is a follower of Jane Deer and wouldtherefore be the recipient of any topic feeds generated by Jane Deer'ssearch activity.

Other Features

User interface displays 3A-3C and 4B show a number of additionalfeatures as well. First, the user interface displays include a number ofnavigation buttons generally indicated at 200. These buttonsillustratively include a “home” button, a “web” button, a “news” button,an “images” button, a “videos” button, a “stream” button, a “people”button, and an “about” button. Of course, these are exemplary buttonsonly and different buttons, additional buttons, or fewer buttons couldbe used as well. In the embodiment shown, the “home” button takes theuser to the user's home page showing the public stream 52 generatedusing topic feeds 70 that were received by that user. The “web” buttontakes the user to a web browser and the “news” button takes the user toa news site that displays news that may be relevant to the user. The“images” and “videos” buttons allow a user to easily confine submittedqueries to look for either images or videos that are relevant to thesearch terms in the query, and the “stream” button allows the user tosearch the user's own public stream 52 for posts relevant to the query.The “people” button allows the user to identify people of interest, thatthe user may wish to follow. The system can also automatically suggestexperts and other people to follow even if the user does not actuate the“people” button. The “about” button describes the functionality of thesystem.

A number of the user interface displays also include additional featureson the bottom of the posts, generally indicated by arrow 204. Theyinclude a “time of post” feature, a “like” feature and a “comment”feature. The “time of post” feature simply indicates the time that apost was posted on the user's public stream 52. The “like” button allowsthe user to indicate that he or she likes the post, and the “comment”button allows the user to comment on the post. This may be done, forinstance, by exposing a text box within which the user can comment onthe post and have that comment published to other recipients. Oneembodiment of this is shown in FIG. 5 . FIG. 5 shows part of a post thatincludes the result 103 discussed above. FIG. 5 also shows that, oncethe user has actuated the “comment” button, a dropdown text box 220appears, which allows the user to enter a textual comment related to thepost 103. The textual comment in box 220 is then distributed toidentified recipients.

More Detailed Embodiment

FIG. 6 illustrates a more detailed block diagram of system 8, andparticularly a more detailed block diagram of one embodiment of publicsearch system 10. Items in FIG. 6 which are similar to those shown inFIG. 1 are similarly numbered. However, FIG. 6 shows that public searchsystem 10 includes a variety of other components as well.

The input from user interface component 22 to public search system 10 isshown not simply as query 60, but as a topic input 210. Topic input 210can be a query, a click, an administrative input, such as the input of auser name or password to log on to the system, an explicit indication ofa topic or person of interest that is to be followed, or a wide varietyof other inputs.

Public search system 10 also includes additional components such as userauthentication component 212 which is used to authenticate user'slogging on to the system. Public search system 10 also includes topicdata collection component 214 which collects various items of data(described below) that are stored in data store 20. System 10 alsoincludes query/result analyzer 216 that can be used to both identify thesubject matter content of queries and results, and to analyze whetherthey should more properly be pursued in a private venue.

Messaging and notification system 218, also included in system 10, isused for receiving and transmitting messages among users of system 10,and also for providing notifications to users in system 10. The messagesand notifications are indicated by block 220.

System 10 also includes topic statistics generator 222 that generates avariety of statistics which will be described below, as well as interesttracking component 224 and suggestion component 226. Interest trackingcomponent 224 processes the various queries and search results that auser interacts with on system 10 to implicitly determine a user'sinterests. These are included, along with interests explicitly input bya user, to not only suggest topics or people to follow, but to alsosuggest changes to search queries that might be input by a user. Thesesuggestions are generated by suggestion component 226.

FIG. 6 also shows that data store 20 has its own index 203. Index 203indexes the information in data store 20 for ease of searching.

FIG. 7 is a flow diagram illustrating one embodiment of the operation ofthe system shown in FIG. 6 . FIGS. 6 and 7 will be described inconjunction with one another. It should be noted, of course, that thefeatures described in FIGS. 6 and 7 can be in addition to, or insteadof, those shown in the previous figures. Also, the particular flow ofoperation described with respect to FIGS. 6 and 7 is illustrative only.In other words, certain steps could be reversed or performed indifferent orders or eliminated or other steps can be added. Similarly,the functions of the various components shown in FIG. 6 could either becombined or split even more finely, using other components. Those shownare shown for exemplary purposes only.

During operation, a user first logs on to system 8, through userinterface component 22, by illustratively performing some type of userauthentication steps. This is managed by user authentication component212 and indicated by block 300 in FIG. 7 . In one embodiment, userauthentication simply requires the user to input a user name andassociated password. User authentication component 212 then compares theuser name and password with profile records stored in data store 20 (oranother data store) to determine that the user is entering a valid username and password. If so, processing continues. If not, the user isprohibited from accessing system 10, until a valid user name andpassword have been entered. Of course, other authentication componentscould be used, such as any type of biometric recognition system, voicerecognition, etc.

Once user authentication has been performed, the user can provide atopic input 210 to public search system 10. The topic input can be aquery, a click on a query, a comment, a click on a query result or aperson, an indication that the user likes a particular post, an explicitindication that the user is interested in a given topic or a person,etc. Any type of input which reflects this type of search activity isreceived by processor 18 and routed to the appropriate components foranalysis and processing. Receiving the topic input is indicated by block302 in FIG. 7 .

FIG. 7 shows that there are a number of different possibilities for thetopic input 210. For instance, the topic input may be a query, or it maybe a click (either on another person's query in a user's public stream,or on a search result that shows up in the user's public stream), it maybe an explicit interest indication by the user indicating that the useris specifically interested in a topic area (such as a person or asubject matter area), or it could be another input. This is indicated byblocks 320, 322, 324, 326 and 328 in FIG. 7 .

Processing a Query

If, at blocks 320 and 322, it is determined that the input is a query,then query processing is performed as shown in FIG. 8 . This isindicated by block 330 in FIG. 7 .

If the input is a query, such as query 60, then the processing describedabove with respect to FIG. 2 is performed. This is indicated by block340 in FIG. 8 . That is, a topic feed 70 is generated for the query 60and recipients of the topic feed are identified and the topic feed 70 isautomatically distributed to those recipients. The query 60 is thenexecuted against a data store 30 and against posts in data store 20 andthe results 80 and 81 are returned to the user. Embodiments of the userinterfaces generated to show this were also described above with respectto FIGS. 3A-5 .

However, FIG. 8 shows that, in another embodiment, additional processingcan be performed as well. For instance, the query 60 can be provided toquery/results analyzer 216 where a linguistic analysis is performed onthe query 60 to identify the topics of interest reflected in the query.In one embodiment, keyword recognition is performed on the query toidentify keywords, that are associated with topics of interest, thatoccur in the query. Of course, more advanced natural language processingand statistical analysis can be performed as well, to identify topics ofinterest. Performing linguistic analysis on the query is indicated byblock 342 in FIG. 8 .

The topics of interest identified in the linguistic analysis are thenoutput to interest tracking component 224 (shown in FIG. 6 ). Interesttracking component 224 is described in greater detail below, withrespect to FIGS. 11 and 12 . Suffice it to say, for now, that interesttracking component 224 receives various items of information based on auser's activity (such as topics of interest reflected in queries orsearch results that the user has interacted with) and identifies areasof interest for the user based on all the information that the user isgenerating, or interacting with. This information is then used by unseencontent of interest tracking component 19 to identify unseen content ofinterest and present it to the user. This is described in greater detailbelow with respect to FIGS. 13-17 . Outputting the results of thelinguistic analysis to the interest tracking component 224 is indicatedby block 344 in FIG. 8 .

FIG. 8 also shows that query/result analyzer 216 can perform additionalprocessing as well. For instance, when using public search system 8, auser may forget that the user's queries are actually being published.Therefore, in one embodiment, query/results analyzer 216 analyzes thequery, and possibly the query results, to determine whether the querymight more appropriately be conducted in private. For instance, the usermay not wish the public to know that he or she is looking for a new job.If the user posts a query such as “where can I automatically update myresume?”, this may give the user's co-workers, and even supervisors,information that the user does not yet wish to be made public. Ofcourse, there are a variety of other subject matter areas that a usermay wish to search, but which the user does not wish to be made public.Therefore, query/results analyzer 216 is illustratively set up toanalyze the text of a query, and the text of results, to determinewhether they are related to subject matter areas that may best be keptprivate. This is indicated by block 346 in FIG. 8 . If not, thenprocessing simply continues at block 354, which is discussed below.

However, if, at block 346, query/results analyzer 216 determines thatthe query or results relate to a subject matter area that the user maywish to be kept private, then query/results analyzer 216 provides anoutput to user interface component 22 that suggests to the user that thequery be pursued privately. This can take the form of a cautionarymessage that is in bold letters, in colored letters, or otherwise. Theoutput may also allow the user to simply click “yes” or “no” to directthe system to a private search forum. Suggesting that the query bepursued privately is indicated by block 348 in FIG. 8 .

If the user does not desire that the query be pursued privately, thenprocessing again simply reverts to block 354. However, if, at block 348,it is determined that the user does wish to have the query pursuedprivately, then processor 18 simply redirects the user to a privatesearch environment, such as by opening a web browser using a privatesearch engine. Determining whether a user wishes to proceed privatelyand, if so, directing the user to a private search environment, isindicated by blocks 350 and 352 in FIG. 8 .

At block 354, data collection component 214 and topic statisticsgenerator 224 collect various items of information from the query (andoptionally the results) and generate desired statistics from thatinformation and update and store the topic and statistics datagenerated, in data store 20. The information is illustratively indexedand the index entries are stored in index 203 as well.

Processing Clicks

Referring again to FIG. 7 , if it is determined at block 324 that thetopic input 210 is not a query, but is instead a click on a query or aclick on a result, then click processing is performed, as indicated atblock 332. One embodiment of click processing is described, in moredetail, in FIG. 9 .

Processor 18 first determines whether the click received as topic input210 was on another user's query. This is indicated by block 550 in FIG.9 . If the input was a click on another user's query, then system 8performs query processing as shown in FIG. 8 , except that it isperformed for the present user (who just clicked on the query) insteadof for the user that previously input the query. For instance, if JohnDoe generates the query “stories about Paul Bunyan” and this is postedto the public stream 52 of Jane Deer, and Jane Deer clicks on thatquery, then query processing is performed in the same way as if JaneDeer had input the query originally, except that topic data collectioncomponent 214 and topic statistics generator 222 generate informationand statistics for Jane Deer that show that she clicked on someoneelse's query, instead of input it herself. Analyzing the text of thequery and returning results, etc., is performed in the same way as shownin FIG. 8 . This is indicated by block 552 in FIG. 9 .

If, at block 550, it is determined that the click was not on another'squery, then processor 18 determines whether the click was on a searchresult input by another. This is indicated by block 554 in FIG. 9 . Ifso, then system 8 performs the same processing as shown in FIG. 4A, fora click on a result. This is indicated by block 556 in FIG. 9 .

FIG. 9 also shows that system 8 can illustratively perform additionalprocessing, based on clicks, as well. It is not only queries input byusers that indicate the interests of the users, but the results that theuser interacts with (e.g., clicks on) also indicate the interests of agiven user. Therefore, query/results analyzer 216 can perform linguisticanalysis on the text of a result that was clicked on to identify thesubject matter corresponding to that result. This is indicated by block558. Those subject matter areas are output to interest trackingcomponent 224 to assist in tracking the interests of the present user.This is indicated by block 560 in FIG. 9 . The operation of interesttracking component 224 is discussed in greater detail below with respectto FIGS. 10 and 11 .

If, at block 554, it is determined that the click was on some otherportion of the user interface display, then processing proceeds withrespect to block 328 in FIG. 7 . This is indicated by block 562 in FIG.9 .

Processing Other Inputs

Referring again to FIG. 7 , if, at block 320, it is determined that theinput 210 is some other type of input, the appropriate action is simplytaken, as indicated by block 336 in FIG. 7 . For instance, FIG. 12 showsa flow diagram illustrating the operation of system 8 when the input 210is a message. In that case, the message is sent to messaging andnotification system 218 and output to the desired recipient. This isindicated by blocks 312, 314 and 316.

Appropriate processing is performed for any other input 210 as well. Forinstance, if the user clicks on the “comment” button and inputs atextual comment, then processor 18 controls system 8 to receive thetextual input, as the comment, through user interface component 22 andidentify recipients that are to receive it and then distribute it tothose recipients.

It should also be noted that system 8 can include other things as well.For instance, though the description has proceeded with respect tosystem 8 receiving mouse clicks, textual inputs, etc., other input andoutput modes could also be used. User interface component 22 can receivespeech input from the user and perform speech recognition, and system 8can be controlled in that way as well. Alternatively, the speechrecognition can be performed in public search system 10. Similarly, userinterface component 22 can include text synthesis components thatsynthesize text into speech and communicate audibly with the user. Awide variety of other changes can also be made to the system.

Data Store 20

FIG. 8A illustrates one embodiment of a number of different items ofinformation that can be stored in topic and statistics data store 20. Ofcourse, the items of information shown in FIG. 8A are all related to anindividual user. Therefore, it can be seen that data store 20illustratively stores all of the queries 60 input by a given user, theclicks on other person's queries and clicks on search results asindicated by 400 in FIG. 8A, all of a user's followers 402, all of thecomments 404 posted by the user, any friends 406 of the user (if friendsare separately designated from followers) the user's interests, bothexplicitly indicated by the user, and implicitly derived by interesttracking component 224, as indicated by block 408 in FIG. 8A, postthread statistics associated with posts that were generated by the user,and the user's status. This is indicated by block 410 in FIG. 8A. Datastore 20 also indicates a user's status as an expert or a guru asindicated by blocks 412 and 416 in FIG. 8A. Data store 20 is shown forexemplary purposes only and other types of data can be stored as well.

Post Thread Statistics

Topic statistics generator 222 illustratively generates post threadstatistics which indicate the number of times that the user's posts havebeen interacted with (such as clicked on or re-posted) by others. Forinstance, if John Doe submits a query 60 which is posted to the publicstream 52 of his followers, and one of the followers (such as Jane Deer)clicks on the query 60, then the query will also be posted on the publicstream 52 of all of the followers of Jane Deer. Thread statistics 410,which are generated by topic statistics generator 222, track how manytimes the user's posts have been posted and re-posted in system 8.

In order to do this, each of the queries (or posts) is stored in datastore 20, in one exemplary embodiment, according to a data structuresuch as that shown in FIG. 8B. It can be seen that the post itself, 500,has an associated root identifier (ID) 502, a relative identifier (ID)504, and a path of relative identifiers (IDs) 506. The root ID 502 forthe post is a unique identifier associated with the author, ororiginator, of the post. In the example being discussed, the root ID 502is that associated with John Doe.

The relative ID for this post 504 is associated with someone downstreamof John Doe who re-posted John Doe's original post. In the example beingdiscussed, the relative ID 504 corresponds to Jane Deer. The path ofrelative IDs 506 extends from the relative ID (the most recent poster)for this post to the root ID 502. For instance, assume that Jane Deer'srelative ID is 14. Then the path of relative ID's 506 is 14, 1. If oneof Jane Deer's followers then re-posts the query, the root ID for there-posted query stays the same (1), the relative ID belongs to thefollower of Jane Deer (say the relative ID for that follower is 28) andthe path of relative ID's is 28, 14, 1. In this way, statisticsgenerator 224 not only keeps track of who originated the posts, but itkeeps track of the number of times the post has been re-posted. It alsokeeps track of the path of followers through which the post traveled.

These types of post thread statistics are of interest for a number ofreasons. For instance, on some social networking sites, when a post ofan individual is widely disseminated, it is referred to as “goingviral.” There can be some prestige associated with a post that has goneviral. However, it can be difficult to identify the originator of thepost. Therefore, using statistics generator 222 and the data structureshown in FIG. 8B (or some similar data structure) system 8 can easilytrack the originator of viral posts, and give the originator credit forthe post threads.

Expert and Guru Status

Expert status 412 and guru status 416 are illustratively assigned tousers that have displayed a great deal of knowledge, or are widelyfollowed, in a given topic area. These users are trusted resources intheir given topic areas. For instance, if John Doe has displayed a greatdeal of knowledge, or is widely followed and, in fact, has a sufficientnumber of followers, in the topic and area of Paul Bunyan, then John Doemay be awarded the expert status 412 in the topic area of Paul Bunyan.If John Doe happens to be the most knowledgeable, or the most followeduser in that subject matter area, then John Doe is illustrativelyawarded the highest (e.g., guru) status 416. This is indicated in datastore 20 as well. Several ways of doing this are described below withrespect to FIG. 13

In any case, data collection component 214 and topic statisticsgenerator 222 can illustratively collect or generate the informationnecessary to award any desired status (for a topic or subject matterarea) to one or more users, based on popularity, or other statistics.

Interest Tracking

To discuss interest tracking reference is again made to FIG. 7 . Recallthat a user can provide an input that explicitly identifies that theuser is interested in something or someone. This is referred to as anexplicit interest indication. If, at block 320, it is determined thatthe input 210 is an explicit interest indication (shown at block 326)then explicit interest tracking is performed as indicated by block 334.FIG. 10 shows a simplified block diagram of one embodiment of interesttracking component 224, and FIG. 11 shows one embodiment of itsoperation. FIG. 10 shows that interest tracking component 224 includesan implicit interest tracking component 580 and an explicit interesttracking component 582. Explicit interest tracking is discussed below,while the operation of implicit tracking component 580 is describedfirst.

As briefly discussed above with respect to FIG. 6 , interest trackingcomponent 224 receives a variety of information and operates on thatinformation to implicitly identify interests of a given user. Byimplicitly identifying interests, it is meant that the user has not madean explicit interest indication indicating that the user is interestedin a certain subject matter area or person but instead component 518implicitly derives that information based on analysis of a user'sactivity.

For instance, a user may explicitly indicate that he or she isinterested in a topic by providing an appropriate input through userinterface component 222. However, implicit interest tracking component580 takes other inputs by the user and analyzes them to implicitlydefine the interests of the user. The information shown in FIG. 10 ,that is considered by component 518, is exemplary only, and other ordifferent information can be used as well. However, the exemplaryinformation shown in FIG. 10 includes textual information from queries584, textual information derived from posts that the user has clicked on586, and textual information from subject matter that the user has“liked” or indicated a preference for 588. The textual information fromqueries 584 can be the results of a grammatical analysis performed onthe queries posted by the user, and may include (by way of example)keywords or predefined topics or people of interest to which the queriesrelate. Similarly, the information from clicks 586 can be grammaticalinformation derived from queries that have been clicked on by the user,or results that have been clicked on by the user. In addition, theinformation from likes 588 can be generated from posts which the userhas “liked” as discussed above with respect to FIGS. 3A-5 .Alternatively, of course, tracking component 224 can receive the rawtext from those sources and submit it to query/results analyzer 216 (oranother component) for grammatical analysis as well. This is indicatedby optional block 602 in FIG. 11 .

Once implicit interest tracking component 580 receives grammaticallyanalyzed text (as indicated by blocks 600 and 602 in FIG. 11 ), it, oranother component, illustratively performs statistical analysis oncontent words of that text to identify implicit topics of interest. Thisis indicated by block 604. For instance, if implicit interest trackingcomponent 580 simply receives a set of keywords that have beengrammatically extracted from the textual sources, then implicit interesttracking component 580 illustratively counts and stores the frequency ofoccurrence of those words in the textual inputs. By identifying thecontent words that are most searched, or otherwise used or interactedwith by a given user, implicit interest tracking component 580 can mapthose words to topics of interest that are recognized in system 8, or itcan generate new topics of interest. For instance, if keywords thatcorrespond to a particular subject matter (such as the words “PaulBunyan”) are frequently used in queries, implicit component 518 canidentify “Paul Bunyan” as a particular subject matter area of interestfor the user. In addition, if the analyzed text includes the name ofanother user (with sufficient frequency) then that user may beidentified as an interest of the current user. Similarly because datastore 20 stores data that identifies other users that have similarinterests to the present user, interest tracking component 224 canimplicitly identify those other users as possible people for the currentuser to “follow”. Further, because system 10 identifies experts and/orgurus associated with topics of interest, system 10 can suggest theseexperts and/or gurus to the user as well. Performing the statisticalanalysis on the content words and other users is indicated by block 604.The same type of analysis can be performed on topics of interest (asopposed to content words) if the topics of interest are provided insteadof just the content words.

Interest tracking component 224 also includes explicit interest trackingcomponent 582. In one illustrative embodiment, a user can input anexplicit interest indication by marking certain textual items,explicitly, as being items of interest to the user. For instance, theuser can use the #tag before, or after, or surrounding, textual words toexplicitly indicate that the user is interested in topics thatcorrespond to those words.

This can also be used to remove certain textual items from the implicitinterest tracking analysis. For instance, if the user inputs a querywhich includes the term “White House”, the user may be referring to thepresident's residence in Washington D.C., or to houses that are white incolor, generally. If the text is not explicitly marked by the user, thenimplicit interest tracking component 580 may either analyze the text andbelieve that the user is interested in the president's residence, or inwhite houses in general. However, if the user explicitly marks the textas follows “#white# #house#” then the term “White House” will be removedfrom the implicit tracking analysis performed by component 580, and theterms “white” and “house” will be input as specifically, and explicitly,marked interests 584 to explicit component 582. Explicit component 582can correlate the marked interest 584 to already defined topics ofinterest, or it can use that information to define a new topic ofinterest that the user can follow.

After it has received the textual inputs and performed the linguisticand statistical processing, interest tracking component 224 generates alist of the top N interests 585 which have been derived for the givenuser. The top N interests will, of course, include all of thoseinterests which have been explicitly indicated by the user. However,they may also include a number of topics of interest that have beenimplicitly derived by component 580. The number, N, of topics ofinterest that are output and stored for a given user can be empiricallyset, or it can be chosen by the user, or it can simply be selected atrandom or any other way. For instance, in one embodiment, interesttracking component 224 keeps track of the top 50 topics of interest fora given user, whether they are implicitly derived or explicitly input.

Once all the inputs have been analyzed, interest tracking component 224combines the implicit topics of interest with the explicit topics ofinterest, as indicated by block 606, and updates data store 20 toindicate the new or revised topics of interest, and also outputs themfor review by the user. This illustratively includes a separate list ofother users who are experts or gurus or simply share the same topics ofinterest. This is indicated by block 608. Interest tracking component224 can do this in a number of different ways. For instance, interesttracking component 224 can automatically update the “Following” list onthe user's home page to include any newly identified topics of interest(subject matter areas or people), and to delete old topics of interest,which no longer fall within the top N topics of interest 585 output bycomponent 224. In this way, system 8 can automatically begin posting newposts to the public stream 52 of the user, to reflect the new,implicitly derived and explicitly indicated topics of interest. Ofcourse, the user may not wish the system to automatically update his orher topics of interest in the “Following” list. Therefore,alternatively, interest tracking component 224 may simply provide anoutput that indicates to the user that certain changes in the user'stopics of interest are suggested, and allow the user to accept or rejectthose changes, either individually, or as a group. This is indicated byblock 610 in FIG. 11 . Component 224 illustratively keeps updating thetop N list 585 as the user uses system 8. In this way, the user caneasily ensure that the public stream 52 contains posts that are ofcurrent interest to the user.

Surfacing Unseen Content of Interest for a Given User

It has been found that it is difficult for a user to perform a searchand only view results that domain experts or gurus (or other trustedsources) have already seen, but that the user has not already seen.FIGS. 13-17 describe a mechanism by which a user can perform a searchand quickly access the best content which has been either implicitly orexplicitly recommended by a domain expert or guru in a given topicspace, without having to do his or her own analysis of the searchresults. It also enables a user to be presented with the search resultsthat have not yet been seen by the user.

FIG. 13 is another illustrative block diagram showing certain items ofpublic search system 10 that can be used to accommodate the user in thisway. Some elements in FIG. 13 are similar to those shown in FIGS. 1 and6 , and are similarly numbered. FIG. 13 , however, also shows unseencontent of interest tracking component 19 in greater detail, includescrawler component 650, and shows that system 10 is connected throughsearch engine 26 to network 28, which is, itself, connected to aplurality of different websites 652 and 654. Each site 652 and 654 hassubstantive content 656 and 658. Sites 652 and 654 (and the associatedcontent) can be part of the corpus to be searched 30 shown in FIGS. 1and 6 , or they can be separate. Content in sites 652 and 654 isidentified by index builder 657 which builds index 659 to the content.In addition, FIG. 13 shows expert/guru data store 660, along with userclick index 662 and unseen content store 664. It will be noted thatthese stores can be part of topic and statics data store 20 shown inFIGS. 1 and 6 , or they can be separate data stores. Similarly,expert/guru store 660 can also correspond to expert status 412 and gurustatus 416 in topic and statics data store 20, or it can be separatefrom those status indicators as well.

In the embodiment shown in FIG. 13 , a user 670 interacts with userinterface component 22 by inputting various inputs to, and receivingvarious outputs from public search system 10. Those inputs and outputscan be those described above and are indicated in FIG. 13 ,collectively, as various inputs and outputs 672. Based on the variousinputs and outputs 672, unseen content of interest tracking component 19identifies unseen content of interest for user 670 and presents theunseen content of interest 674 to user 670 through user interfacecomponent 22.

FIG. 13 shows that unseen content of interest tracking component 19includes expert identifier component 676. Expert identifier component676, itself, includes manual component 678 and machine component 680.Expert identifier component 676 identifies trusted resources (such asexperts and gurus) in various subject matter areas and stores them asexpert status 412 and guru status 416 in expert/guru store 660 (whichcan be part of data store 20). Expert identifier component 676 canidentify experts algorithmically using machine component 680.

Particular ways in which trusted resources (e.g., expert status 412 andgum status 416) are identified by machine component 680 can vary widely.As discussed above, they may simply have to do with the number offollowers a given user has on a given subject. Of course, they may alsobe determined based on the post thread statistics (the number of postsor re-posts attributed to that user) on that given topic or subjectmatter area. Other techniques can be used as well, in order to recognizesomeone as an expert or guru. For instance, a community of users canvote on that status by entering appropriate inputs on interface 22, orthe status can be awarded in other ways as well.

In addition, an expert or gum can be determined algorithmically, basedupon the user's interactions with public search system 8. For instance,it is believed that the level of detail in a user's queries on a givensubject matter area reflects the user's depth of knowledge in that area.That is, if a user is relatively new to a subject matter area, thatuser's queries tend to be shorter and broader in linguistic content.However, if the user is quite knowledgeable in that subject matter area,then the queries input by the user tend to be longer and more specific.Therefore, in accordance with one embodiment, an ontology is developedfor various topics. When a user inputs a query related to one of thosetopics, a natural language processing system parses the query andapplies it to the ontology for that subject matter area. The ontologymay illustratively be a graph of linguistic elements, such as words,which start at a first level that reflects relatively littleunderstanding of the topic area represented by the ontology. Thelinguistic elements in the graph, at deeper levels, correspond to a morein-depth knowledge of the topic area represented by the ontology.Therefore, when a user inputs a query, the natural language processingsystem applies the linguistic content of the query against the ontology.If the query matches one of the initial levels in the ontology, then theuser is deemed to have relatively little knowledge of that subjectmatter area. However, if the query descends more deeply into theontology, and matches a deeper level of the ontology, then the user isdeemed to have a more in-depth knowledge of the topic area representedby the ontology. In one embodiment, if the user submits enough queriesthat descend deeply enough into the ontology, then the user is deemed tobe an expert or guru in the topic area represented by the ontology. Ofcourse, the depth of the ontology and the number of queries that descendto that depth (in order to identify a user as a trusted resource such asan expert or guru) can be empirically determined, and can be differentfor different applications, and even for different subject matter areas.This mechanism for identifying experts and gurus is exemplary only.

In another embodiment, a weighted, inverted index is generated basedupon the queries input by each given user. The inverted index is formedof bi-grams (two-word units) used by the user in the input queries. Thebi-grams are weighted based on their frequency of occurrence in thequeries input by the users. Bi-grams are also associated with thedifferent topic areas. Therefore, the weighted bi-grams in the indexwill represent the amount of search activity performed by the given userin the different topic areas. If a user shows a sufficiently large levelof activity in a given topic space, then that user may be identified asan expert or guru in that topic space. Again, the level of activityrequired to identify a user as an expert or guru may be empiricallydetermined, or determined otherwise, and it may vary based onapplication or based on subject matter area.

Yet another embodiment for identifying experts or gurus is simplythrough the direct use of human knowledge. For instance, it may bewidely known in a community of users that a given individual is anexpert in a subject matter or topic area. Those individuals can bemanually identified as experts or gurus using manual component 678, orthey can be recruited to participate in the community of users of system10 and to identify themselves as experts or gurus. Again, this is butone exemplary mechanism for identifying an individual as an expert orguru.

In one embodiment, manual component 678 simply generates an appropriateuser interface at user interface component 22 to allow a user orcommunity of users to identify an expert in a given area. In any case,the experts and gurus are stored in store 660, and are associated withthe given subject matter or topic areas for which they are experts orgurus.

Component 19 is also shown in FIG. 13 as including click index generator682. Click index generator 682 generates an index of all the informationthat a given user 670 has clicked on or interacted with through userinterface component 22. This information is stored in user click index662.

FIG. 13 also shows that component 19 includes new content identifier684. Crawler component 650 crawls the content indexed in index 659 toidentify new content that has been added. The new content is provided tonew content identifier 684, which identifies content of interest in thevarious subject matter or topic areas that are of interest to users ofsystem 10. As will be described below, new content identifier 684determines, based on the user click index 662 and based on the outputsfrom interest tracking component 224, which users would find the newcontent of interest. It then identifies whether the individual usershave seen each item of content and stores unseen content of interest forthe various users in unseen content store 664. Then, when a given userbegins using system 10, component 19 presents the unseen content ofinterest 674 to the given user in a desired way.

FIG. 14 is a flow diagram illustrating one embodiment of the overalloperation of the system shown in FIG. 13 , in presenting unseen contentof interest 674 to a given user 670. Crawler component 650 first crawlsthe index of content 659 to identify content that is associated with thedifferent topic areas or subject matter areas that are of interest tousers of system 10. In addition, of course, crawler 650 can crawl theindex of content to identify content related to additional subjectmatter areas or topics of interest as well, even though they are notcurrently of interest to any user of system 10.

The information generated by crawler component 650 is then provided toexpert identifier 676 can use machine component 680 to analyze the textof the content and, if the text reflects a sufficient level ofknowledge, then the author of the content can be identified as an expertor guru for that subject matter or topic of interest, and stored in datastore 660. Of course, as discussed above, expert identifier component676 also identifies experts using manual component 678 or by analyzingquery and search interactions with system 10 as well. Crawling the indexof content and identifying experts in topic areas is indicated by blocks690 and 692 in FIG. 14 . Interest tracking component 224 also identifiestopics of interest for the given user 670. This is discussed above withrespect to FIG. 10 , and is indicated by block 694 in FIG. 14 .

Click index generator 682 receives and stores click data showing thecontent that the given user 670 has already accessed (such as clicked onor otherwise interacted with). Click index generator 682 then storesthis information in user click index 662. This allows system 10 to trackthe content that a user 670 has already seen, and is indicated by block696 in FIG. 14 .

New content identifier 684 then identifies content of interest providedby crawler 650, which may be of interest for the given user 670, basedupon the topics of interest or subject matter areas of interest to theuser 670. This is indicated by block 698 in FIG. 14 . It will be notedthat this can be done in a number of different ways. This is describedin greater detail below with respect to FIG. 15 .

In any case, once content of interest has been identified for a user,then new content identifier 684 compares that content with the contentthat has already been seen by user 670, based upon the information inuser click index 662. New content identifier 684 thus obtains a list ofcontent of interest that has not already been seen by user 670. This isindicated by block 700 in FIG. 14 . It should be noted that new contentidentifier 684 can include all of the content that relates to a topic ofinterest or subject matter area of interest to a user, and simplyemphasize the items of content that have not been seen by the user, orit can explicitly exclude certain items from the list. For example, inan instance where the user is following an evolving story, news articlesthat are written earlier in time may not necessarily be of interestanymore, relative to those that have been written more recently in time.In that case, content identifier 684 can expressly exclude those earlierarticles from the list of content of interest that is presented to theuser. Alternatively, new content identifier 684 can present all of thecontent of interest to the user and simply highlight the unseen contentof interest 674 in a suitable way, such as by providing it higher up inthe displayed list of returned content, such as by displaying it in boldletters, or otherwise. The optional step of excluding non-useful contentis indicated by block 702 in FIG. 14 .

Then, the next time the user is ready to view the unseen content ofinterest 674, component 19 presents the list of unseen content ofinterest 674 to user 670, through user interface component 22. This isindicated by block 704 in FIG. 14 .

In one illustrative embodiment, component 19 explicitly indicates whythis list of content is being presented to the user. For instance, wherean article has just been written by an expert in one of the topic areasof interest to a user 670, component 19 may display a link to thatarticle along with a description of why the article is being displayedto the user. By way of example, the explanation might read “This articleis being presented to you because it was written by John Q. Public, whois an expert in the area of Paul Bunyan.” Of course, there are a widevariety of other ways to explain why any given item of unseen content isbeing presented to the user, and a textual description is but oneexample. Explicitly indicating why a list of content is being presentedto the user is indicated by block 706 in FIG. 14 .

FIG. 15 is a block diagram illustrating a number of differentembodiments for identifying content of interest for a given user 670 (asindicated by block 698 in FIG. 14 ). In one embodiment, an expert 708provides an input to system 10 (through user interface component 22 ordirectly to new content identifier 684, or otherwise) that indicatesthat the expert 708 has interacted with an item of content in apredefined way. For instance, it may be that the expert 708 conducted asearch in one of his or her areas of expertise and clicked on a searchresult that was returned in response to the search. In that case, thissearch result is identified by new content identifier 684 as beingcontent of interest in that specific subject matter or topic area. Inanother embodiment, the user may simply click on, or “like”, or commenton a post 54 in the expert's public stream 52. New content identifier684 then identifies the post that was interacted with by expert 708 ascontent of interest for the identified subject matter or topic area.Identifying content of interest related to a given subject matter ortopic area based on an expert interaction with that item of content isindicated by block 710 in FIG. 15 .

In another embodiment, crawler component 650 can identify an item ofcontent (such as an article or a comment in the public search stream 52of expert 708, or any other item of content, that was authored by expert708). In that case, the item of content that is authored by expert 708can be identified by new content identifier 684 as content of interest.This is indicated by block 712 in FIG. 15 .

In yet another embodiment, crawler component 650 can identifyinformation on a third party site 714 (such as another social networkingsite or blog site) or new content identifier 684 can receive informationdirectly from the third party site 714. By way of example, assume thatexpert 708 also authors or contributes to a blog on another site or is amember of another social network. The content of interest might be apost, outside of pubic search system 10, but instead on third party site714 that hosts the blog or the alternate social network. Posts orcomments by expert 708 on that third party site 714 can either bedirectly provided to new content identifier 684 or they can be providedby crawler component 650 crawling the third party site 714. In eithercase, the content that was authored or interacted with by the expert 708on third party site 714 can be identified by new content identifier 684as content of interest for the given topic area or subject matter area.This is indicated by blocks 716 in FIG. 15 .

Finally, content identifier 684 can identify new content of interest inany other way. This is indicated by block 718 in FIG. 15 . It will benoted that, in each of the examples shown in FIG. 15 , natural languageprocessing can be conducted on these items to determine whether theyrelate to the subject matter area for which expert 708 is identified asan expert.

Once the content has been identified as content of interest, then it ismarked as content of interest for a given user. This is indicated byblock 720 in FIG. 15 .

FIG. 16 is a flow diagram illustrating one embodiment in which trackingcomponent 19 presents the unseen content of interest to user 670 throughuser interface component 22 (as indicated by block 704 in FIG. 14 ).FIG. 16 indicates a variety of different embodiments for providing thisinformation.

In one embodiment, user 670 logs on to system 10 as described above.This is indicated by block 722 in FIG. 16 . Then, component 19 andpublic search system 10 can provide the unseen content of interest 674to user 670 in a variety of different ways. In one embodiment, system 10waits until user 670 inputs a query on one of the user's topics ofinterest. Receiving such a query is indicated by block 724 in FIG. 16 .In response, public search system 10 identifies search results relatedto the query as described above with respect to FIGS. 1 and 6 , and alsoidentifies unseen content of interest from store 664 that is responsiveto the query. This is indicated by block 726 in FIG. 16 . The systemthen outputs the search results with emphasis on the unseen content ofinterest 674, through user interface component 22, to user 670. This isindicated by block 728 in FIG. 16 . Again, this can be done bypresenting only the unseen content of interest, or by emphasizing theunseen content of interest by ranking it higher on the search resultspage, or by otherwise emphasizing it such as providing it in boldletters, in shaded or highlighted characters or otherwise.

In another embodiment, after the user logs on to system 10 at block 72,system 10 waits until the user clicks on the topic (or person) that theuser is following. For instance, as shown in the user interface displaysof FIGS. 3A-4B, system 10 can provide a user interface displayindicating particular people or other topics of interest that the userhas explicitly or implicitly indicated interest in. When the user logson to the system and clicks on one of those areas, then public searchsystem 10 can provide the unseen content of interest 674 via userinterface component 22 to user 670. In that case, the results providedto the user 670 will be only those related to the particular topic ofinterest that the user has clicked on. Receiving the user click andgenerating the display are indicated by blocks 730 and 732 in FIG. 16 ,respectively.

In yet another embodiment, system 10 provides the unseen content ofinterest 674, using a specialized display, as soon as the user 670 logsonto the system. For instance, once user 670 logs on, system 10 mayprovide a user interface display on (for instance) half of the screen,that indicates that there is new content of interest on given subjectmatter areas that are of interest to the user, that the user has not yetseen. This can be done automatically, as soon as the user logs in. Thedisplay will illustratively include links so user 670 can easilynavigate to the unseen content of interest easily, by simply clicking ona link. This is indicated by block 734 in FIG. 16 .

In yet another embodiment, system 10 can generate the display of unseencontent of interest 674 and simply post it as a post 54 in the user'spublic stream 52. Therefore, as soon as the user logs on to the systemas shown at step 722, and the user's public stream 52 is displayed, oneof the posts 54 in public stream 52 will be the unseen content ofinterest in each of the topic areas or subject matter areas that are ofinterest to the user. This is indicated by block 736 in FIG. 16 .

In any of the embodiments in FIG. 16 , the system illustrativelyindicates, explicitly, why the content is being displayed. This wasdiscussed above with respect to block 706 in FIG. 14 , and it isindicated by the same numeral in FIG. 16 .

FIG. 17 illustrates one exemplary embodiment of how system 10 can assistuser 670 in arriving at relevant search results more quickly. In oneembodiment, expert identifier component 676 not only identifies expertsand gurus and stores them in store 660, but it also tracks all of thetheir queries that are related to the topic of interest for which theyare deemed an expert or guru. By doing so, component 676 stores thevarious query progressions of the given experts or gurus in expert queryprogression store 661. For instance, it may be that an expert or guruputs in a first query but does not get the desired search results. Inthat case, the expert may revise the query slightly and resubmit it.Again, the expert may not get the desired search results. The expert maythen revise the query a second time and submit it to the searchcomponent and finally arrive at desired results which the expertinteracts with. By saving this query progression, system 10 can assistuser 670 in arriving at relevant search results more quickly. FIG. 17illustrates one way of doing this.

In the embodiment shown in FIG. 17 , public search system 10 firstreceives a user query, or query progression that relates to a givensubject matter or topic of interest. This is indicated by block 734 inFIG. 17 . As discussed above with respect to other features, system 10can use a natural language processing system to identify the subjectmatter of the queries in the query progression. Of course, the queryprogression received by the user may be only a single query or it may bea progression of multiple queries. In any case, system 10 compares thereceived query progression from user 670 with the stored queryprogression used by experts in the subject matter area of the queryprogression input by the user. This is indicated by block 736 in FIG. 17.

If a matching query progression is found in store 661, system 10 can doone of a variety of different things. For instance, system 10 can outputthe results (also illustratively stored in store 661) that were finallyinteracted with at the end of the expert's query progression. Forinstance, when the expert has ended his or her query progression andfinally interacted with some of the search results presented, thosesearch results can automatically be presented to the user 670 as soon asthe query progression of the user 670 matches the stored queryprogression of an expert in store 661.

Of course, user 670 illustratively need not duplicate the entire queryprogression of the expert. Instead, when the user submits one query thatwas perhaps the beginning of the query progression for the expert,system 10 can automatically retrieve and send the results of the finalquery in the expert's query progression to user 670. Alternatively,system 10 may wait for the user to progress through a number ofdifferent modifications within the query progression, before presentingthe final search results of the expert's query progression. In any case,outputting the results that the expert eventually arrived at isindicated by block 738 in FIG. 17 .

In another embodiment, instead of providing the results that werespawned from the final query in the expert's query progression, system10 can simply suggest additional or different queries based upon thecomparison with the expert's query progression. For instance, system 10can generate a user interface suggesting that the user replace his orher initial query with the final query in the matching expert queryprogression. Suggesting additional queries is indicated by block 740 inFIG. 17 . Of course, system 10 can assist the user at arriving atrelevant search results, based upon a comparison with an expert queryprogression, in other ways as well.

Enterprise Search

It should be noted that while system 10 is described above as beingcompletely public, it can also be public within a given context. Forinstance, system 10 can be deployed behind a firewall so only potentialrecipients that also reside behind the firewall will receive topic feed70. This allows those in, for example, an organization to share searchactivity but keep that information behind the firewall. Thus, employeesof a company can collaborate and have frank discussions and conductshared search activity about competitors without providing thecompetitors with access to sensitive information. System 10 can also bedeployed on even a smaller scale, such as within a work group.

Illustrative Computing Environment

FIG. 18 shows one illustrative computing environment where system 8 canbe employed. The computing environment can be employed as public searchsystem 10, user interface component 22, or both. Similarly, thosecomponents can be deployed on other type of computing devices, such ashandheld devices, mobile devices, laptop devices, cellular telephones,personal digital assistants (PDA), etc.

With reference to FIG. 18 , an exemplary system for implementing someembodiments includes a general-purpose computing device in the form of acomputer 810. Components of computer 810 may include, but are notlimited to, a processing unit 820 (which can act as processor 18) asystem memory 830, and a system bus 821 that couples various systemcomponents including the system memory to the processing unit 820. Thesystem bus 821 may be any of several types of bus structures including amemory bus or memory controller, a peripheral bus, and a local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus also known as Mezzanine bus.

Computer 810 typically, but not always, includes a variety of computerreadable media. Computer readable media can be any available media thatcan be accessed by computer 110 and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer readable media may comprise computerstorage media and communication media. Computer storage media includesboth volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 810. Communication media (which isnot included in computer storage media) typically embodies computerreadable instructions, data structures, program modules or other data ina modulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 831and random access memory (RAM) 832. A basic input/output system 833(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 810, such as during start-up, istypically stored in ROM 831. RAM 832 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 820. By way of example, and notlimitation, FIG. 13 illustrates operating system 834, applicationprograms 835, other program modules 836, and program data 837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 18 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 851that reads from or writes to a removable, nonvolatile magnetic disk 852,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 856 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 841 is typically connectedto the system bus 821 through a non-removable memory interface such asinterface 840, and magnetic disk drive 851 and optical disk drive 855are typically connected to the system bus 821 by a removable memoryinterface, such as interface 850.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 18 , provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 18 , for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837. Operating system 844, application programs 845, other programmodules 846, and program data 847 are given different numbers here toillustrate that, at a minimum, they are different copies.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures, such as a parallel port, game port or a universal serial bus(USB). A monitor 891 or other type of display device is also connectedto the system bus 821 via an interface, such as a video interface 890.In addition to the monitor, computers may also include other peripheraloutput devices such as speakers 897 and printer 896, which may beconnected through an output peripheral interface 895.

The computer 810 can be operated in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 880. The remote computer 880 may be a personal computer, ahand-held device, a server, a router, a network PC, a peer device orother common network node, and typically includes many or all of theelements described above relative to the computer 810. The logicalconnections depicted in FIG. 18 include a local area network (LAN) 871and a wide area network (WAN) 873, but may also include other networks.Such networking environments are commonplace in offices, enterprise-widecomputer networks, intranets and the Internet. Computer 810 can be usedin many different applications. For instance, by way of example, andwithout limitation, it can be used for general purpose computing, datacommunication applications, in avionics, military applications orelectronics, or shipping electronics. Of course, computer 810, orportions thereof, can be used in many other applications as well.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. The modem 872, which may be internal orexternal, may be connected to the system bus 821 via the user inputinterface 860, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 810, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 18 illustrates remoteapplication programs 885 as residing on remote computer 880. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

Computer 810 may also act as one of the servers or server computersdiscussed with respect to FIG. 18 . Also, it should be noted that manyof the components shown in FIG. 18 can be fully implemented in silicon,or partially implemented in silicon. The particular configuration shownin FIG. 18 is exemplary only. The embodiments described above in FIGS.1-17 can also be implemented by the processor and using memory and othercomponents in FIG. 18 .

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A computer-implemented method comprising:detecting a set of queries input by a first user in an electronic searchsystem; based on linguistic analysis of the set of queries,automatically identifying a subject matter area reflected in the set ofqueries, and generating a search activity metric that is associated withthe first user and corresponds to the subject matter area; based on adetermination that the search activity metric meets a threshold,automatically identifying the first user as a trusted resource for thesubject matter area; detecting past content interaction activity, of asecond user, that interacted with content in the electronic searchsystem; automatically identifying an area of interest of the second userbased on the past content interaction activity; determining, with acomputer processor, that the area of interest of the second user iscorrelated to the subject matter area based on a match threshold;automatically identifying, with the computer processor, unseen contentof interest in the subject matter area based on a determination that:the first user, identified as a trusted resource for the subject matterarea, has previously interacted with the unseen content of interest inthe subject matter area, and the second user has not already interactedwith the unseen content of interest in the subject matter area; andgenerating a user interface display that presents the unseen content ofinterest to the second user, wherein the user interface display includesan indication that the first user, identified as a trusted resource inthe subject matter area, has previously interacted with the unseencontent of interest.
 2. The computer-implemented method of claim 1wherein the electronic search system comprises a public search system,and further comprising: assigning a level of knowledge in the subjectmatter area based on content of the set of queries; and determining thatthe level of knowledge surpasses a threshold level and, based on thatdetermination, identifying an individual responsible for the set ofqueries as the first user identified as a trusted resource for thesubject matter area.
 3. The computer-implemented method of claim 1wherein determining that the area of interest of the second user iscorrelated to the subject matter area comprises: implicitly deriving thesubject matter area based on an analysis of computing activity of thefirst user; and implicitly deriving the area of interest of the seconduser based on an analysis of computing activity of the second user. 4.The computer-implemented method of claim 1, wherein identifying theunseen content of interest comprises: automatically identifying an itemof content as validated by the first user; and comparing the item ofcontent to the past content interaction activity of the second user. 5.The computer-implemented method of claim 1, wherein the electronicsearch system is configured to generate one or more public streams foreach of the first user and the second user, each public stream of theone or more public streams having posts therein, and further comprising:receiving an input by the first user indicative of the interaction witha post located in a public stream of the first user; identifying thepost as content that the first user interacted with; and indicating tothe second user that the first user interacted with the post.
 6. Thecomputer-implemented of claim 5, wherein interaction with a postcomprises authoring the post.
 7. The computer-implemented method ofclaim 1, wherein interacting with unseen content of interest comprisesselecting the unseen content for viewing.
 8. The computer-implementedmethod of claim 1 wherein the electronic search system comprises apublic search system that displays a public search stream to the seconduser having posts therein, and wherein generating the user interfacedisplay comprises: generating the user interface as a post to the publicsearch stream in response to a query by the second user.
 9. A electronicsearch system, comprising: a user interface component; a searchcomponent configured to: detect a set of queries input by a first user;search an electronic data store; and return corresponding search resultsresponsive to the search; an implicit interest tracking componentconfigured to implicitly derive a topic of interest based on linguisticanalysis of the set of queries; a seen results data store configured tostore information indicative of search results that the first user hasinteracted with and an unseen content of interest tracking componentconfigured to: identify a second user as a trusted resource in the topicof interest based on detected past content interaction activity of thesecond user, in the electronic search system; identify new content thatthe second user interacted with in relation to the topic of interest;and compare the new content to the information in the seen results datastore to determine whether the first user has already interacted withthe new content, and generate a user interface using the user interfacecomponent to display the new content to the first user with anindication of whether the second user has interacted with the newcontent and an indication of that the first user has not interacted withthe new content.
 10. The electronic search system of claim 9 and furthercomprising: a topic feed generator that generates a public stream thathas posts that include the set of queries in the electronic searchsystem by the first user and the corresponding results, wherein theunseen content of interest tracking component identifies the new contentfrom the posts of the public stream.
 11. The electronic search system ofclaim 10 wherein the unseen content of interest tracking componentidentifies search results returned in response to a query submitted bythe second user.
 12. The computer-implemented method of claim 2, whereinidentifying the first user, as a trusted resource comprises analyzingthe set of queries received in the electronic search system to obtainquery progressions of the first user in the subject matter area.
 13. Theelectronic search system of claim 9, wherein the implicit interesttracking component receives inputs from the first user and analyzes theinputs to identify the topic of interest to the first user.
 14. Theelectronic search system of claim 13, wherein the inputs aregrammatically analyzed.
 15. The electronic search system of claim 14,wherein the grammatical analysis includes linguistic processing.
 16. Theelectronic search system of claim 14, wherein the grammatical analysisincludes statistical processing.
 17. The electronic search system ofclaim 13, wherein the inputs from the first user comprises textualinformation the first user has clicked on and the textual information isgrammatically analyzed.
 18. The electronic search system of claim 13,and further comprising an explicit interest tracking componentconfigured to receive an explicit interest indication from the firstuser to identify the topic of interest to the first user.
 19. Theelectronic search system of claim 13, and further comprising an explicitinterest tracking component configured to receive an explicitnon-interest indication from the first user to identify a topic ofnon-interest to the first user.
 20. The electronic search system ofclaim 19, wherein the explicit interest tracking component is furtherconfigured to identify the topic of interest to the first user based onthe explicit non-interest indication from the first user.